Towards understanding of poly-guanine activated fluorescent silver nanoclusters
Bibliographic record
Abstract
It has been recently reported that the fluorescence of some DNA-templated silver nanoclusters (AgNCs) can be significantly enhanced upon by hybridizing with a partially complementary DNA containing a G-rich overhang near the AgNCs. This discovery has found a number of analytical applications but many fundamental questions remain to be answered. In this work, the photostability of these activated AgNCs is reported. After adding the G-rich DNA activator, the fluorescence intensity peaks in ∼1 h and then starts to decay, where the decaying rate is much faster with light exposure. The lost fluorescence is recovered by adding NaBH4, suggesting that the bleaching is an oxidative process. Once activated, the G-rich activator can be removed while the AgNCs still maintain most of their fluorescence intensity. UV-vis spectroscopy suggests that new AgNC species are generated upon hybridization with the activator. The base sequence and length of the template DNA have also been varied, leading to different emission colors and color change after hybridization. G-rich aptamers can also serve as activators. Our results indicate that activation of the fluorescence by G-rich DNA could be a convenient method for biosensor development since the unstable NaBH4 is not required for the activation step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".